Progress in the Application of Brain-Computer Interface in the Diagnosis and Treatment of Parkinson's Disease

Abstract

Parkinson's disease (PD) is a common neurodegenerative disease, whose main features include movement disorders, cognitive impairment and other non-motor symptoms. With the aging of the population, the incidence of PD has increased year by year, seriously affecting the quality of life of patients. At present, although treatments such as levodopa, deep brain stimulation (DBS) and rehabilitation training can relieve some symptoms, it is difficult to prevent the progression of the disease, and the demand for personalized treatment is increasing. Therefore, neuroregulatory technology based on brain-computer interface (BCI) has become an important research direction in the diagnosis and treatment of PD. BCI technology has shown important application value in disease diagnosis, motor function rehabilitation and cognitive intervention of PD. BCI technology based on electroencephalogram (EEG) can be used to detect abnormal EEG signal patterns in PD patients and assist in early screening of the disease; combined with neuroregulatory technology (such as transcranial magnetic stimulation TMS and functional electrical stimulation FES), BCI can effectively improve the motor function of PD patients. However, the current application of BCI in PD still faces challenges such as insufficient signal decoding accuracy, unstable data quality and poor patient adaptability. Therefore, this article reviews the latest research progress of BCI in the field of PD diagnosis and treatment, and explores its technical bottlenecks and future development directions, in order to provide valuable reference for research in this field.